This disclosure relates generally to the field of digital photography. More particularly, but not by way of limitation, this disclosure relates to still image stabilization techniques. As used herein, image stabilization refers to a collection of techniques for reducing motion-induced blurring during image capture operations. Such motion may result from the movement of the camera, objects in the scene, or both.
Taking high quality photographs in low ambient light conditions, or photographing dynamic scenes (e.g., sport scenes) is challenging due to camera motion and/or the motion of objects within a scene during image capture. One way to reduce motion blur without amplifying an image's noise is to capture and fuse multiple short exposed images of the scene. Such operations are often called ‘Still Image Stabilization.’ While shortening image exposure times can reduce motion blur artifacts, it does so at the expense of a noisier and/or darker image.
A common approach to image stabilization consists of (1) selecting a reference image from a set of multiple short exposed images, (2) globally registering all non-reference images with respect to the reference image, and (3) synthesizing an output image by fusing all captured images to the reference image. In this way the output image represents the scene as it was at the time the reference image was captured, where non-reference images are used to reduce the noise in the reference image by averaging/merging multiple observations of each reference pixel across all images.
A common approach to globally registering non-reference images with respect to the reference image is to use pixel information for the registration. Such a method is generally referred to as pixel-based registration. A pixel-based registration method involves registering non-reference images by matching their corresponding features with the reference image. This procedure has the advantage of being able to match with high accuracy those areas of the image which are the most visually relevant (e.g. textures, edges, corners). This is because the procedure is specifically based on matching features such as corners. However, although offering precision for close objects, pixel-based registration has the disadvantage of being limited in the amount of relative motion it can detect between two images. Larger motions between two different images may not be detectable by this procedure. In addition, rolling shutter distortion is also difficult to detect using a pixel-based registration approach. Moreover, currently used pixel-based registration methods are inefficient and can be improved.